OpenRaiser/Intern-Atlas huggingface dataset:
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
Venue: arXiv cs.LG 2020
Year: 2020

Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference results. In this paper, we investigate and propose two causes of this problem: (1) The ELBO objective cannot utilize the label information directly. (2) A bottleneck value exists and continuing to optimize ELBO after this value will not improve inference accuracy. On the basis of the experiment results, we propose SHOT-VAE to address these problems without introducing additional prior knowledge. The SHOT-VAE offers two contributions: (1) A new ELBO approximation named smooth-ELBO that integrates the label predictive loss into ELBO. (2) An approximation based on optimal interpolation that breaks the ELBO value bottleneck by reducing the margin between ELBO and the data likelihood. The SHOT-VAE achieves good performance with a 25.30% error rate on CIFAR-100 with 10k labels and reduces the error rate to 6.11% on CIFAR-10 with 4k labels.
CancerNet-SCa: Tailored Deep Neural Network Designs for Detection of Skin Cancer from Dermoscopy Images
Venue: arXiv cs.CV 2020
Year: 2020

Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S., with not only significant effects on health and well-being but also significant economic costs associated with treatment. A crucial step to the treatment and management of skin cancer is effective skin cancer detection due to strong prognosis when treated at an early stage, with one of the key screening approaches being dermoscopy examination. Motivated by the advances of deep learning and inspired by the open source initiatives in the research community, in this study we introduce CancerNet-SCa, a suite of deep neural network designs tailored for the detection of skin cancer from dermoscopy images that is open source and available to the general public as part of the Cancer-Net initiative. To the best of the authors' knowledge, CancerNet-SCa comprises of the first machine-designed deep neural network architecture designs tailored specifically for skin cancer detection, one of which possessing a self-attention architecture design with attention condensers. Furthermore, we investigate and audit the behaviour of CancerNet-SCa in a responsible and transparent manner via explainability-driven model auditing. While CancerNet-SCa is not a production-ready screening solution, the hope is that the release of CancerNet-SCa in open source, open access form will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.
Explainable Composition of Aggregated Assistants
Venue: arXiv cs.AI 2020
Year: 2020

A new design of an AI assistant that has become increasingly popular is that of an "aggregated assistant" -- realized as an orchestrated composition of several individual skills or agents that can each perform atomic tasks. In this paper, we will talk about the role of planning in the automated composition of such assistants and explore how concepts in automated planning can help to establish transparency of the inner workings of the assistant to the end-user.
Exploring Voice Conversion based Data Augmentation in Text-Dependent Speaker Verification
Venue: arXiv cs.SD 2020
Year: 2020

In this paper, we focus on improving the performance of the text-dependent speaker verification system in the scenario of limited training data. The speaker verification system deep learning based text-dependent generally needs a large scale text-dependent training data set which could be labor and cost expensive, especially for customized new wake-up words. In recent studies, voice conversion systems that can generate high quality synthesized speech of seen and unseen speakers have been proposed. Inspired by those works, we adopt two different voice conversion methods as well as the very simple re-sampling approach to generate new text-dependent speech samples for data augmentation purposes. Experimental results show that the proposed method significantly improves the Equal Error Rare performance from 6.51% to 4.51% in the scenario of limited training data.
Semantic-Based VPS for Smartphone Localization in Challenging Urban Environments
Venue: arXiv cs.RO 2020
Year: 2020

Accurate smartphone-based outdoor localization system in deep urban canyons are increasingly needed for various IoT applications such as augmented reality, intelligent transportation, etc. The recently developed feature-based visual positioning system (VPS) by Google detects edges from smartphone images to match with pre-surveyed edges in their map database. As smart cities develop, the building information modeling (BIM) becomes widely available, which provides an opportunity for a new semantic-based VPS. This article proposes a novel 3D city model and semantic-based VPS for accurate and robust pose estimation in urban canyons where global navigation satellite system (GNSS) tends to fail. In the offline stage, a material segmented city model is used to generate segmented images. In the online stage, an image is taken with a smartphone camera that provides textual information about the surrounding environment. The approach utilizes computer vision algorithms to rectify and hand segment between the different types of material identified in the smartphone image. A semantic-based VPS method is then proposed to match the segmented generated images with the segmented smartphone image. Each generated image holds a pose that contains the latitude, longitude, altitude, yaw, pitch, and roll. The candidate with the maximum likelihood is regarded as the precise pose of the user. The positioning results achieves 2.0m level accuracy in common high rise along street, 5.5m in foliage dense environment and 15.7m in alleyway. A 45% positioning improvement to current state-of-the-art method. The estimation of yaw achieves 2.3° level accuracy, 8 times the improvement to smartphone IMU.
An Empirical Study on Failed Error Propagation in Java Programs with Real Faults
Venue: arXiv cs.SE 2020
Year: 2020

During testing, developers can place oracles externally or internally with respect to a method. Given a faulty execution state, i.e., one that differs from the expected one, an oracle might be unable to expose the fault if it is placed at a program point with no access to the incorrect program state or where the program state is no longer corrupted. In such a case, the oracle is subject to failed error propagation. We conducted an empirical study to measure failed error propagation on Defects4J, the reference benchmark for Java programs with real faults, considering all 6 projects available (386 real bugs and 459 fixed methods). Our results indicate that the prevalence of failed error propagation is negligible when testing is performed at the unit level. However, when system-level inputs are provided, the prevalence of failed error propagation increases substantially. This indicates that it is enough for method postconditions to predicate only on the externally observable state/data and that intermediate steps should be checked when testing at system level.
Spatially Correlated Patterns in Adversarial Images
Venue: arXiv cs.AI 2020
Year: 2020

Adversarial attacks have proved to be the major impediment in the progress on research towards reliable machine learning solutions. Carefully crafted perturbations, imperceptible to human vision, can be added to images to force misclassification by an otherwise high performing neural network. To have a better understanding of the key contributors of such structured attacks, we searched for and studied spatially co-located patterns in the distribution of pixels in the input space. In this paper, we propose a framework for segregating and isolating regions within an input image which are particularly critical towards either classification (during inference), or adversarial vulnerability or both. We assert that during inference, the trained model looks at a specific region in the image, which we call Region of Importance (RoI); and the attacker looks at a region to alter/modify, which we call Region of Attack (RoA). The success of this approach could also be used to design a post-hoc adversarial defence method, as illustrated by our observations. This uses the notion of blocking out (we call neutralizing) that region of the image which is highly vulnerable to adversarial attacks but is not important for the task of classification. We establish the theoretical setup for formalising the process of segregation, isolation and neutralization and substantiate it through empirical analysis on standard benchmarking datasets. The findings strongly indicate that mapping features into the input space preserves the significant patterns typically observed in the feature-space while adding major interpretability and therefore simplifies potential defensive mechanisms.
Control and implementation of fluid-driven soft gripper with dynamic uncertainty of object
Venue: arXiv cs.RO 2020
Year: 2020

Soft grippers, for stable grasping of objects, with high compliance could be considered a suitable candidate for replacement of conventional rigid grippers, and in recent years, they have been emerging exponentially in industries. Not only are these highly adaptable grippers capable of static grasping of an object, but also they can be utilized for performing object manipulation tasks. Plenty of contemporary studies have been emphasizing on static grasping ability of soft grippers. However, in this thesis, planar in-hand object manipulation in a soft gripper which comprises a pair of soft finger is investigated.
Efficiently Estimating a Sparse Delay-Doppler Channel
Venue: arXiv cs.IT 2020
Year: 2020

Multiple wireless sensing tasks, e.g., radar detection for driver safety, involve estimating the "channel" or relationship between signal transmitted and received. In this work, we focus on a certain channel model known as the delay-doppler channel. This model begins to be useful in the high frequency carrier setting, which is increasingly common with developments in millimeter-wave technology. Moreover, the delay-doppler model then continues to be applicable even when using signals of large bandwidth, which is a standard approach to achieving high resolution channel estimation. However, when high resolution is desirable, this standard approach results in a tension with the desire for efficiency because, in particular, it immediately implies that the signals in play live in a space of very high dimension $N$ (e.g., ~$10^6$ in some applications), as per the Shannon-Nyquist sampling theorem. To address this difficulty, we propose a novel randomized estimation scheme called Sparse Channel Estimation, or SCE for short, for channel estimation in the $k$-sparse setting (e.g., $k$ objects in radar detection). This scheme involves an estimation procedure with sampling and space complexity both on the order of $k(logN)^3$, and arithmetic complexity on the order of $k(log N)^3 + k^2$, for $N$ sufficiently large. To the best of our knowledge, Sparse Channel Estimation (SCE) is the first of its kind to achieve these complexities simultaneously -- it seems to be extremely efficient! As an added advantage, it is a simple combination of three ingredients, two of which are well-known and widely used, namely digital chirp signals and discrete Gaussian filter functions, and the third being recent developments in sparse fast fourier transform algorithms.
Use of Student's t-Distribution for the Latent Layer in a Coupled Variational Autoencoder
Venue: arXiv cs.LG 2020
Year: 2020

A Coupled Variational Autoencoder, which incorporates both a generalized loss function and latent layer distribution, shows improvement in the accuracy and robustness of generated replicas of MNIST numerals. The latent layer uses a Student's t-distribution to incorporate heavy-tail decay. The loss function uses a coupled logarithm, which increases the penalty on images with outlier likelihood. The generalized mean of the generated image's likelihood is used to measure the performance of the algorithm's decisiveness, accuracy, and robustness.
Zero-Shot Learning with Knowledge Enhanced Visual Semantic Embeddings
Venue: arXiv cs.CV 2020
Year: 2020

We improve zero-shot learning (ZSL) by incorporating common-sense knowledge in DNNs. We propose Common-Sense based Neuro-Symbolic Loss (CSNL) that formulates prior knowledge as novel neuro-symbolic loss functions that regularize visual-semantic embedding. CSNL forces visual features in the VSE to obey common-sense rules relating to hypernyms and attributes. We introduce two key novelties for improved learning: (1) enforcement of rules for a group instead of a single concept to take into account class-wise relationships, and (2) confidence margins inside logical operators that enable implicit curriculum learning and prevent premature overfitting. We evaluate the advantages of incorporating each knowledge source and show consistent gains over prior state-of-art methods in both conventional and generalized ZSL e.g. 11.5%, 5.5%, and 11.6% improvements on AWA2, CUB, and Kinetics respectively.
Analysis of Movement-Based Connectivity Restoration Problem in Wireless Ad-Hoc and Sensor Networks
Venue: arXiv cs.NI 2020
Year: 2020

Topology control, including topology construction and maintenance phases, is a vital conception for wireless ad-hoc networks of any kind, expressly the wireless sensor networks (WSN). Topology maintenance, the latter phase, concerns several problems, such as optimizing the energy consumption, increasing the data rate, making clusters, and sustaining the connectivity. A disconnected network, among other strategies, can efficiently be connected again using a Movement-based Connectivity Restoration (MCR) method, where a commensurate number of nodes move (or are moved) to the desired positions. However, finding an optimal route for the nodes to be moved can be a formidable problem. As a matter of fact, this paper presents details regarding a direct proof of the NP-Completeness of the MCR Problem by a reduction of the well-studied Steiner Tree Problem using the minimum number of Steiner points and the bounded edge length.
An Exploration of the Heterogeneous Unsourced MAC
Venue: arXiv cs.IT 2020
Year: 2020

The unsourced MAC model was originally introduced to study the communication scenario in which a number of devices with low-complexity and low-energy wish to upload their respective messages to a base station. In the original problem formulation, all devices communicate using the same information rate. This may be very inefficient in certain wireless situations with varied channel conditions, power budgets, and payload requirements at the devices. This paper extends the original problem setting so as to allow for such variability. More specifically, we consider the scenario in which devices are clustered into two classes, possibly with different SNR levels or distinct payload requirements. In the cluster with higher power,devices transmit using a two-layer superposition modulation. In the cluster with lower energy, users transmit with the same base constellation as in the high power cluster. Within each layer, devices employ the same codebook. At the receiver, signal groupings are recovered using Approximate Message Passing(AMP), and proceeding from the high to the low power levels using successive interference cancellation (SIC). This layered architecture is implemented using Coded Compressed Sensing(CCS) within every grouping. An outer tree code is employed to stitch fragments together across times and layers, as needed.This pragmatic approach to heterogeneous CCS is validated numerically and design guidelines are identified.
BigBen: Telemetry Processing for Internet-wide Event Monitoring
Venue: arXiv cs.NI 2020
Year: 2020

This paper describes BigBen, a network telemetry processing system designed to enable accurate and timely reporting of Internet events (e.g., outages, attacks and configuration changes). BigBen is distinct from other Internet-wide event detection systems in its use of passive measurements of Network Time Protocol (NTP) traffic. We describe the architecture of BigBen, which includes (i) a distributed NTP traffic collection component, (ii) an Extract Transform Load (ETL) component, (iii) an event identification component, and (iv) a visualization and reporting component. We also describe a cloud-based implementation of BigBen developed to process large NTP data sets and provide daily event reporting. We demonstrate BigBen on a 15.5TB corpus of NTP data. We show that our implementation is efficient and could support hourly event reporting. We show that BigBen identifies a wide range of Internet events characterized by their location, scope and duration. We compare the events detected by BigBen vs. events detected by a large active probe-based detection system. We find only modest overlap and show how BigBen provides details on events that are not available from active measurements. Finally, we report on the perspective that BigBen provides on Internet events that were reported by third parties. In each case, BigBen confirms the event and provides details that were not available in prior reports, highlighting the utility of the passive, NTP-based approach.
Modular Moose: A new generation software reverse engineering environment
Venue: arXiv cs.SE 2020
Year: 2020

Advanced reverse engineering tools are required to cope with the complexity of software systems and the specific requirements of numerous different tasks (re-architecturing, migration, evolution). Consequently, reverse engineering tools should adapt to a wide range of situations. Yet, because they require a large infrastructure investment, being able to reuse these tools is key. Moose is a reverse engineering environment answering these requirements. While Moose started as a research project 20 years ago, it is also used in industrial projects, exposing itself to all these difficulties. In this paper we present ModMoose, the new version of Moose. ModMoose revolves around a new meta-model, modular and extensible; a new toolset of generic tools (query module, visualization engine, ...); and an open architecture supporting the synchronization and interaction of tools per task. With ModMoose, tool developers can develop specific meta-models by reusing existing elementary concepts, and dedicated reverse engineering tools that can interact with the existing ones.
SAMA-VTOL: A new unmanned aircraft system for remotely sensed data collection
Venue: arXiv cs.CV 2020
Year: 2020

In recent years, unmanned aircraft systems (UASs) are frequently used in many different applications of photogrammetry such as building damage monitoring, archaeological mapping and vegetation monitoring. In this paper, a new state-of-the-art vertical take-off and landing fixed-wing UAS is proposed to robust photogrammetry missions, called SAMA-VTOL. In this study, the capability of SAMA-VTOL is investigated for generating orthophoto. The major stages are including designing, building and experimental scenario. First, a brief description of design and build is introduced. Next, an experiment was done to generate accurate orthophoto with minimum ground control points requirements. The processing step, which includes automatic aerial triangulation with camera calibration and model generation. In this regard, the Pix4Dmapper software was used to orientate the images, produce point clouds, creating digital surface model and generating orthophoto mosaic. Experimental results based on the test area covering 26.3 hectares indicate that our SAMA-VTOL performs well in the orthophoto mosaic task.
Who's Who in the Information Technology Research in the Philippines A Social Network Analysis
Venue: arXiv cs.CY 2020
Year: 2020

This study reported the conference papers presented conducted by the two computing societies in the Philippines. Toward this goal, all published conference proceedings from the National Conference of IT Education and Philippine Computing Society Conference were gathered and analyzed using social network analysis. The findings of the study disclosed that there are 733 papers presented in the conference for the span of 18 years. On the average, both conferences had 27 papers presented annually. Private higher education institutions dominated the list of research productive schools where De La Salle University tops the list. A researcher in the University of the Philippines-Diliman is the most prolific researcher with 39 publications and "algorithm" was the most researched topic. Researchers tend to work in small team consisting of 2 to 3 members. Implications and limitations of the study are also presented.
Cross-Domain Generalization Through Memorization: A Study of Nearest Neighbors in Neural Duplicate Question Detection
Venue: arXiv cs.CL 2020
Year: 2020

Duplicate question detection (DQD) is important to increase efficiency of community and automatic question answering systems. Unfortunately, gathering supervised data in a domain is time-consuming and expensive, and our ability to leverage annotations across domains is minimal. In this work, we leverage neural representations and study nearest neighbors for cross-domain generalization in DQD. We first encode question pairs of the source and target domain in a rich representation space and then using a k-nearest neighbour retrieval-based method, we aggregate the neighbors' labels and distances to rank pairs. We observe robust performance of this method in different cross-domain scenarios of StackExchange, Spring and Quora datasets, outperforming cross-entropy classification in multiple cases.
TaiJi: Longest Chain Availability with BFT Fast Confirmation
Venue: arXiv cs.CR 2020
Year: 2020

Most state machine replication protocols are either based on the 40-years-old Byzantine Fault Tolerance (BFT) theory or the more recent Nakamoto's longest chain design. Longest chain protocols, designed originally in the Proof-of-Work (PoW) setting, are available under dynamic participation, but has probabilistic confirmation with long latency dependent on the security parameter. BFT protocols, designed for the permissioned setting, has fast deterministic confirmation, but assume a fixed number of nodes always online. We present a new construction which combines a longest chain protocol and a BFT protocol to get the best of both worlds. Using this construction, we design TaiJi, the first dynamically available PoW protocol which has almost deterministic confirmation with latency independent of the security parameter. In contrast to previous hybrid approaches which use a single longest chain to sample participants to run a BFT protocol, our native PoW construction uses many independent longest chains to sample propose actions and vote actions for the BFT protocol. This design enables TaiJi to inherit the full dynamic availability of Bitcoin, as well as its full unpredictability, making it secure against fully-adaptive adversaries with up to 50% of online hash power.